Machinery monitoring systems have been permanently installed in, inter alia, today's large process plants, power generation stations and pipelines in an attempt to provide machinery protection by continuously monitoring the behavior and performance characteristics of machinery at a multiplicity of points and possibly acquiring data from these points simultaneously. More recently, the trend has been to enhance the monitoring systems by directly interfacing computers to the systems for periodically collecting data from these systems for historical trend, machinery diagnostics and predictive maintenance purposes. However, these current systems use methods which only retain a small history of machine performance at best.
For example, current systems periodically collect, store, display and print machinery data in a variety of formats using a variety of schemes. One such scheme is to continuously sample and store data at a high sample rate to obtain data with relatively high data time resolution, and as storage space fills, to replace the stored data with a new data set. This scheme does not automatically store the historical information necessary to analyze one or more problems, may not represent a long enough period of time to represent the on-set of one or more problems and does not readily identify the occurrence of one or more problems.
Another scheme is to intermittently capture data "snapshots" of the machine performance. A small set of "snapshots" are maintained in memory and saved in the event of a machine problem. However, the time represented by the "snapshots" may not be adequate to represent historical machine performance or may not represent a continuous set of data with the machine fault occurring between data sets previously stored in memory.
A common scheme is to represent the machine performance with an overall magnitude, eliminating all of the details that are contained to generate the magnitude. Although the magnitude can be used for protection, it does little to identify the causes of the problem.
The disadvantage of these schemes is they either consume too much memory, may not provide a rapid method to identify when one or more problems commence and to describe its progress or may lose the ability to diagnose one or more problems after the fact by either destroying the data with replacement information, or by taking data samples with the data of interest falling between the samples.
Therefore, if one were to continuously capture the machine data using current techniques, the memory requirements of such data storage can be enormous considering that the data is preferably collected over a period of months or years. In addition, long transmission times are required for transmitting large quantities of continuous machine data to a remote data base for permanent storage and with enough detail and history to perform fault analysis and diagnosis.
In addition, with current systems it is a challenge to capture and store infrequently occurring machine anomalies and to ensure that these anomalous events get managed using past learning experiences and procedures according to historical data. For example, the cause of and the procedures needed to deal with these machine anomalies may not be repetitive enough to stay within peoples' memory. Further, to make matters worse, many anomalous events occur so infrequently that people who managed and learned from previous situations have either changed jobs or are not available by the time a similar anomalous event occurs again. These anomalous events can have a profound impact if not managed correctly. For example, improper management of one of these anomalous events may cause loss of life, loss of property, fugitive emissions and other undesirable consequences.
Therefore, what is needed is a system which, inter alia, allows machine data to be compressed and stored in a reduced form which represents a continuous set of data correlative to a continuous history of machine performance without allowing machine faults between data sets to go undetected and thus unrepresentable. In addition, a need exists for a system which reduces data volume sufficiently to allow transmission using commonly available transmission media. Furthermore, a need exists for a system which allows stored compressed data to be retrieved and reconstructed to provide a complete continuous waveshape history of machine performance. Moreover, a system is needed which provides continuous data acquisition for diagnostic and predictive maintenance purposes for maximizing the machine's life while minimizing its cost and averting any catastrophic events when in operation.
The following prior art reflects the state of the art of which applicant is aware and is included herewith to discharge applicant's acknowledged duty to disclose relevant prior art. It is stipulated, however, that none of these references teach singly nor render obvious when considered in any conceivable combination the nexus of the instant invention as disclosed in greater detail hereinafter and as particularly claimed.
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The patent to Cubbins, et al., U.S. Pat. No. 4,908,785 issued Mar. 13, 1990, teaches the use of a data compression method for telemetry of vibration data. The method achieves compression by filtering the incoming signal to extract a low frequency band. This low frequency band is sent to a multiplexed system without encryption or compression but can be sampled at a lower frequency since the upper frequency has been significantly reduced. The total range of frequencies is then divided, either by fractional octave filters, DFT or FFT to amplitude detect bands of frequencies and then the magnitude of the signals in this band or bands are extracted. These magnitudes are multiplexed with the lower frequency signals to give an overall or specific distribution of energy. Once processed, the low frequency data can be extracted but a waveshape can not be generated from the information present.
The other prior art listed above but not specifically described further catalog the prior art of which the applicant is aware. These references diverge even more starkly from the references specifically distinguished above.